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[X86/ARM] add gru mode for rnn (#7026)
* [X86] Add GRU for RNN, complete elementwise op, move cast from arm to host, fix precision_profile bug * pre-commit * [ARM] add RNN-GRU OP; Optimize RNN-GRU OP * fix complie bug * fix elementwise left problem * merge develop * fix windows ci * change arm cast test to host cast test
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| // Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. | ||
| // | ||
| // Licensed under the Apache License, Version 2.0 (the "License"); | ||
| // you may not use this file except in compliance with the License. | ||
| // You may obtain a copy of the License at | ||
| // | ||
| // http://www.apache.org/licenses/LICENSE-2.0 | ||
| // | ||
| // Unless required by applicable law or agreed to in writing, software | ||
| // distributed under the License is distributed on an "AS IS" BASIS, | ||
| // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| // See the License for the specific language governing permissions and | ||
| // limitations under the License. | ||
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| #pragma once | ||
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| #include "lite/backends/arm/math/sgemm.h" | ||
| #ifdef LITE_WITH_ARM | ||
| #include <arm_neon.h> | ||
| #endif | ||
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| namespace paddle { | ||
| namespace lite { | ||
| namespace arm { | ||
| namespace math { | ||
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| template <typename T> | ||
| struct RNNGRUValue { | ||
| const T* gate_weight; | ||
| const T* state_weight; | ||
| const T* reset_bias; | ||
| T* gate_value; | ||
| T* reset_output_value; | ||
| T* output_value; | ||
| const T* prev_out_value; | ||
| }; | ||
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| template <typename T> | ||
| void rnn_activation(const T* din, | ||
| T* dout, | ||
| int size, | ||
| lite_api::ActivationType act_type, | ||
| int threads) { | ||
| switch (act_type) { | ||
| case lite_api::ActivationType::kSigmoid: | ||
| act_sigmoid(din, dout, size, threads); | ||
| break; | ||
| case lite_api::ActivationType::kSigmoid_v2: | ||
| act_sigmoid(din, dout, size, threads); | ||
| break; | ||
| case lite_api::ActivationType::kTanh: | ||
| act_tanh(din, dout, size, threads); | ||
| break; | ||
| case lite_api::ActivationType::kTanh_v2: | ||
| act_tanh(din, dout, size, threads); | ||
| break; | ||
| case lite_api::ActivationType::kRelu: | ||
| act_relu(din, dout, size, threads); | ||
| break; | ||
| default: | ||
| LOG(FATAL) << "unsupport activation type:" << static_cast<int>(act_type); | ||
| break; | ||
| } | ||
| } | ||
|
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| template <typename T> | ||
| void compute_kernel(RNNGRUValue<T> value, | ||
| int frame_size, | ||
| int batch_size, | ||
| lite_api::ActivationType active_node, | ||
| lite_api::ActivationType active_gate) { | ||
| auto value_reset_gate = value.gate_value; | ||
| auto value_update_gate = value.gate_value + frame_size; | ||
| auto value_reset_output = value.reset_output_value; | ||
| auto value_reset_bias = value.reset_bias; | ||
| auto cell_state_value = value.gate_value + 2 * frame_size; | ||
| auto value_output = value.output_value; | ||
| auto value_prev_out = value.prev_out_value; | ||
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| for (int b = 0; b < batch_size; b++) { | ||
| rnn_activation(value_reset_gate, | ||
| value_reset_gate, | ||
| frame_size, | ||
| lite_api::ActivationType::kSigmoid_v2, | ||
| 1); | ||
| rnn_activation(value_update_gate, | ||
| value_update_gate, | ||
| frame_size, | ||
| lite_api::ActivationType::kSigmoid_v2, | ||
| 1); | ||
|
|
||
| for (int i = 0; i < frame_size; i++) { | ||
| value_reset_output[i] = | ||
| (value_reset_output[i] + value_reset_bias[i]) * value_reset_gate[i]; | ||
| cell_state_value[i] += value_reset_output[i]; | ||
| } | ||
|
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| rnn_activation(cell_state_value, | ||
| cell_state_value, | ||
| frame_size, | ||
| lite_api::ActivationType::kTanh_v2, | ||
| 1); | ||
|
|
||
| if (value.prev_out_value) { | ||
| for (int i = 0; i < frame_size; i++) { | ||
| value_output[i] = (1.f - value_update_gate[i]) * cell_state_value[i] + | ||
| value_update_gate[i] * value_prev_out[i]; | ||
| } | ||
| } else { | ||
| for (int i = 0; i < frame_size; i++) { | ||
| value_output[i] = (1.f - value_update_gate[i]) * cell_state_value[i]; | ||
| } | ||
| } | ||
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| value_reset_gate += frame_size * 3; | ||
| value_update_gate += frame_size * 3; | ||
| value_reset_output += frame_size; | ||
| cell_state_value += frame_size * 3; | ||
| value_output += frame_size; | ||
| if (value.prev_out_value) { | ||
| value_prev_out += frame_size; | ||
| } | ||
| } | ||
| } | ||
|
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| template <> | ||
| void compute_kernel<float>(RNNGRUValue<float> value, | ||
| int frame_size, | ||
| int batch_size, | ||
| lite_api::ActivationType active_node, | ||
| lite_api::ActivationType active_gate) { | ||
| auto value_reset_gate = value.gate_value; | ||
| auto value_update_gate = value.gate_value + frame_size; | ||
| auto value_reset_output = value.reset_output_value; | ||
| auto value_reset_bias = value.reset_bias; | ||
| auto cell_state_value = value.gate_value + 2 * frame_size; | ||
| auto value_output = value.output_value; | ||
| auto value_prev_out = value.prev_out_value; | ||
| int i = 0; | ||
| float32x4_t vec_one = vdupq_n_f32(1.f); | ||
|
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| for (int b = 0; b < batch_size; b++) { | ||
| rnn_activation(value_reset_gate, | ||
| value_reset_gate, | ||
| frame_size, | ||
| lite_api::ActivationType::kSigmoid_v2, | ||
| 1); | ||
| rnn_activation(value_update_gate, | ||
| value_update_gate, | ||
| frame_size, | ||
| lite_api::ActivationType::kSigmoid_v2, | ||
| 1); | ||
|
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||
| for (i = 0; i + 3 < frame_size; i += 4) { | ||
| float32x4_t vec_out = vld1q_f32(value_reset_output + i); | ||
| float32x4_t vec_reset = vld1q_f32(value_reset_gate + i); | ||
| float32x4_t vec_bias = vld1q_f32(value_reset_bias + i); | ||
| vec_out = vmulq_f32(vaddq_f32(vec_out, vec_bias), vec_reset); | ||
| vst1q_f32(value_reset_output + i, vec_out); | ||
| vst1q_f32(cell_state_value + i, | ||
| vaddq_f32(vec_out, vld1q_f32(cell_state_value + i))); | ||
| } | ||
| for (; i < frame_size; i++) { | ||
| value_reset_output[i] = | ||
| (value_reset_output[i] + value_reset_bias[i]) * value_reset_gate[i]; | ||
| cell_state_value[i] += value_reset_output[i]; | ||
| } | ||
|
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||
| rnn_activation(cell_state_value, | ||
| cell_state_value, | ||
| frame_size, | ||
| lite_api::ActivationType::kTanh_v2, | ||
| 1); | ||
|
|
||
| if (value.prev_out_value) { | ||
| for (i = 0; i + 3 < frame_size; i += 4) { | ||
| float32x4_t vec_vug = vld1q_f32(value_update_gate + i); | ||
| float32x4_t vec_vpo = vld1q_f32(value_prev_out + i); | ||
| float32x4_t vec_csv = vld1q_f32(cell_state_value + i); | ||
| vec_vpo = vmulq_f32(vec_vug, vec_vpo); | ||
| float32x4_t vec_out = | ||
| vmlaq_f32(vec_vpo, vsubq_f32(vec_one, vec_vug), vec_csv); | ||
| vst1q_f32(value_output + i, vec_out); | ||
| } | ||
| for (; i < frame_size; i++) { | ||
| value_output[i] = (1.f - value_update_gate[i]) * cell_state_value[i] + | ||
| value_update_gate[i] * value_prev_out[i]; | ||
| } | ||
| } else { | ||
| for (i = 0; i + 3 < frame_size; i += 4) { | ||
| float32x4_t vec_vug = vld1q_f32(value_update_gate + i); | ||
| float32x4_t vec_csv = vld1q_f32(cell_state_value + i); | ||
| float32x4_t vec_out = vmulq_f32(vsubq_f32(vec_one, vec_vug), vec_csv); | ||
| vst1q_f32(value_output + i, vec_out); | ||
| } | ||
| for (; i < frame_size; i++) { | ||
| value_output[i] = (1.f - value_update_gate[i]) * cell_state_value[i]; | ||
| } | ||
| } | ||
|
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| value_reset_gate += frame_size * 3; | ||
| value_update_gate += frame_size * 3; | ||
| value_reset_output += frame_size; | ||
| cell_state_value += frame_size * 3; | ||
| value_output += frame_size; | ||
| if (value.prev_out_value) { | ||
| value_prev_out += frame_size; | ||
| } | ||
| } | ||
| } | ||
|
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||
| template <typename T> | ||
| struct RnnGruUnitFunctorV2 { | ||
| static void compute(ARMContext* ctx, | ||
| RNNGRUValue<T> value, | ||
| int frame_size, | ||
| int batch_size, | ||
| lite_api::ActivationType active_node, | ||
| lite_api::ActivationType active_gate) { | ||
| if (value.prev_out_value) { | ||
| operators::ActivationParam act_param; | ||
| act_param.has_active = false; | ||
| lite::arm::math::sgemm(false, | ||
| true, | ||
| batch_size, | ||
| frame_size, | ||
| frame_size, | ||
| 1.f, | ||
| value.prev_out_value, | ||
| frame_size, | ||
| value.state_weight, | ||
| frame_size, | ||
| 0.f, | ||
| value.reset_output_value, | ||
| frame_size, | ||
| nullptr, | ||
| false, | ||
| act_param, | ||
| ctx); | ||
| } | ||
| compute_kernel(value, frame_size, batch_size, active_node, active_gate); | ||
| } | ||
| }; | ||
|
|
||
| } // namespace math | ||
| } // namespace arm | ||
| } // namespace lite | ||
| } // namespace paddle |
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